Abstract: [Purpose] Rock Quality Designation (RQD) serves as a fundamental index in geotechnical engineering for evaluating rock mass integrity. It is extensively applied in rock mass classification systems and serves as a critical input parameter for various engineering rating methods. Conventionally, RQD determination relies on manual logging of recovered drill cores. However, this approach is labor-intensive, time-consuming, and often sensitive to drilling techniques and core quality. Such dependencies introduce subjectivity and potential inconsistencies, ultimately limiting the objectivity and repeatability of RQD evaluation.[Method] In light of these challenges, this study proposes an innovative, nondestructive approach utilizing deep learning. We adopt the YOLOv5 (You Only Look Once, version 5) framework to detect and localize discontinuities directly from borehole televiewer images, thereby eliminating the need for physical core extraction. First, raw televiewer imagery is preprocessed, annotated, and augmented to build a representative dataset that highlights natural fractures, bedding planes, and other geological discontinuities. Next, a YOLOv5 detector is trained on this dataset to recognize and segment discontinuities with high spatial accuracy. Finally, the model output is post-processed to compute RQD automatically, by quantifying the proportion of continuous rock segments exceeding the standard 10?cm threshold.[Results] To assess the method’s performance, a case study was conducted on borehole zk4, part of a tunnel project in Yongzhou City, Hunan Province, China. Intelligent RQD values derived from the televiewer images were compared with conventional RQD measurements obtained from core boxes in the field. The results indicate that the automated approach tends to overestimate RQD by around 20?% relative to manual measurements, with a mean absolute error of 9.82?%. Despite this systematic bias, the spatial trend of RQD variation identified by the intelligent method closely matches that of in-situ wave velocity profiles, suggesting that the technique accurately captures relative changes in rock mass properties along the borehole.[Conclusion] Overall, the proposed YOLOv5based workflow effectively reduces the influence of drilling-induced biases and core extraction artifacts on RQD estimation. By enabling rapid, repeatable, and objective computation of RQD directly from borehole images, the method enhances both efficiency and reliability of rock quality assessment. Future work will explore calibration strategies to adjust for systematic deviations and integration with complementary geophysical datasets. This approach demonstrates significant potential to digitalize geotechnical investigation processes, streamline tunnel engineering workflows, and advance rock mass characterization in a more robust and data-driven manner.